EP 3699921 A1 20200826 - OPTIMIZING CATHETERIZATION LABORATORY THROUGHPUT USING MACHINE LEARNING
Title (en)
OPTIMIZING CATHETERIZATION LABORATORY THROUGHPUT USING MACHINE LEARNING
Title (de)
OPTIMIERUNG DES KATHETERISIERUNGSLABORDURCHSATZES MITHILFE VON MASCHINELLEM LERNEN
Title (fr)
OPTIMISATION DU RENDEMENT DE LABORATOIRE DE CATHÉTÉRISME À L'AIDE DE L'APPRENTISSAGE MACHINE
Publication
Application
Priority
EP 19464002 A 20190222
Abstract (en)
Systems and methods for determining one or more measures of interest for optimizing throughput of a catheterization laboratory are provided.A priorimedical procedure data relating to a medical procedure to be performed on a patient in a catheterization laboratory is received. One or more measures of interest are predicted based on the receiveda priorimedical procedure data using a trained machine learning model. The one or more measures of interest include an overall time for performing the medical procedure on the patient in the catheterization laboratory. The one or more predicted measures of interest are output.
IPC 8 full level
G16H 40/20 (2018.01); G16H 20/40 (2018.01)
CPC (source: EP)
G16H 20/40 (2017.12); G16H 40/20 (2017.12)
Citation (search report)
- [Y] US 2008221830 A1 20080911 - ILKIN HAKAN MEHMET [US]
- [Y] US 2019013095 A1 20190110 - LAWRIE JOCK [AU]
- [A] US 2018253531 A1 20180906 - SHARMA PUNEET [US], et al
- [A] US 2007067194 A1 20070322 - DANEHORN KENNETH [SE], et al
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
DOCDB simple family (publication)
DOCDB simple family (application)
EP 19464002 A 20190222